CN115616624A - Humidity inversion method and device based on GNSS-IR dual-frequency data fusion and storage medium - Google Patents
Humidity inversion method and device based on GNSS-IR dual-frequency data fusion and storage medium Download PDFInfo
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Abstract
The application relates to a humidity inversion method, a humidity inversion device and a storage medium based on GNSS-IR dual-frequency data fusion, and belongs to the technical field of soil humidity measurement. The application includes: the method comprises the steps of simultaneously receiving direct signals and reflected signals of a satellite by adopting an antenna to obtain signal-to-noise ratio data, obtaining two characteristic parameters of amplitude and phase of a first waveband and a second waveband through the signal-to-noise ratio data, fusing the two characteristic parameters to obtain fused characteristic parameters, establishing a multivariate linear model through the fused characteristic parameters, inverting the soil humidity based on the multivariate linear model, and compared with single-waveband data measurement in the prior art, the method improves the corresponding data quality through quality complementation by adopting a double-waveband data fusion mode, and improves the precision of a measurement result.
Description
Technical Field
The application belongs to the technical field of soil humidity measurement, and particularly relates to a humidity inversion method and device based on GNSS-IR dual-frequency data fusion and a storage medium.
Background
The GNSS-IR (Global Navigation Satellite System-reflection) technology, i.e., the Global Satellite Navigation System reflected signal technology, is a novel remote sensing technology, and has the advantages of low experimental cost, strong adaptability and the like, so that the technology is rapidly developed. The L wave band used by the GNSS receiver has the characteristic of strong penetrability, so that the GNSS receiver can penetrate through ground covering vegetation or snow layer and other obstacles to a certain extent, and ground reflection signals received by the GNSS receiver after ground reflection of wave band signals have certain relation with ground physical parameters. Besides the characteristic of strong penetrability, the L wave is sensitive to the change of the electromagnetic property of the surface of an observation medium, so that the technology is developed in the field of monitoring the change of soil humidity in recent years;
in the existing GNSS-IR technology, single-band signal data are generally used for measurement, and the defects of unstable data quality and low precision exist.
Disclosure of Invention
Therefore, the application provides a humidity inversion method, a humidity inversion device and a storage medium based on GNSS-IR dual-frequency data fusion, and aims to solve the problems that in the prior art, single-band signal data are used for measurement, the data quality is unstable, and the measurement result precision is not high.
In order to achieve the purpose, the following technical scheme is adopted in the application:
the method for inverting the humidity based on GNSS-IR dual-frequency data fusion comprises the following steps:
simultaneously receiving direct signals and corresponding reflected signals of a satellite through a GNSS signal receiver, and obtaining original signal-to-noise ratio data according to the direct signals and the reflected signals;
respectively obtaining the amplitude and the phase of a first wave band and the amplitude and the phase of a second wave band through original signal-to-noise ratio data;
fusing the amplitudes of the first wave band and the second wave band to obtain fused amplitudes, and fusing the phases of the first wave band and the second wave band to obtain fused phases;
establishing a multivariate linear model according to the fused amplitude and phase;
and substituting the fused amplitude and phase into a multivariate linear model to obtain a corresponding soil humidity value.
Preferably, the first and second electrodes are formed of a metal,
the obtaining the amplitude and the phase of the first waveband and the amplitude and the phase of the second waveband respectively through the original signal-to-noise ratio data comprises:
processing the original signal-to-noise ratio data to obtain the phase difference between the reflected signal and the direct signal;
removing the influence of the direct signal on the reflected signal in the original signal-to-noise ratio data to obtain a multipath reflected signal;
and fitting the multipath reflected signals by a least square method, and obtaining the corresponding amplitudes and phases of the first wave band and the second wave band according to the phase difference of the direct signals and the reflected signals.
Preferably, the first and second liquid crystal display panels are,
and respectively fusing the amplitudes and the phases of the first wave band and the second wave band by adopting a Helmert fusion method.
Preferably, the first and second liquid crystal display panels are,
performing Helmert fusion on the amplitudes and the phases of the first wave band and the second wave band respectively, and obtaining the fused amplitudes and phases comprises:
respectively establishing random models between the random models and the in-situ soil humidity according to the respective amplitudes and phases of the first wave band and the second wave band;
determining a corresponding coefficient matrix through a random model;
establishing an error equation according to the coefficient matrix;
obtaining a normal equation according to an error equation;
establishing a relation between the residual sum of squares and the unit total variance according to a normal equation;
iterative calculation is carried out on the process from the error equation to the relation between the residual sum of squares and the unit total variance to obtain corresponding weight;
and obtaining the corresponding fused amplitude and phase through the corresponding weights.
Preferably, the first and second liquid crystal display panels are,
the establishing of the multivariate linear model according to the fused amplitude and phase comprises the following steps:
obtaining an observed value of soil humidity according to the fused amplitude and phase;
estimating the observed value to obtain a predicted value of the soil humidity;
obtaining the deviation square sum of the observed value and the estimated value according to the observed value and the estimated value;
according to the principle of a least square method, the sum of squares of the deviations is minimized to obtain a regression coefficient;
and establishing the multivariate linear model by taking the fused amplitude and phase as independent variables of the multivariate linear model, taking soil humidity as a dependent variable and the determined regression coefficient.
Preferably, the first and second electrodes are formed of a metal,
the processing the original signal-to-noise ratio data to obtain the phase difference between the reflected signal and the direct signal comprises:
obtaining the path of the reflected signal reaching the antenna of the GNSS signal receiver according to the height H from the antenna of the GNSS signal receiver to the reflecting surface and the included angle theta between the direct signal and the reflecting surface;
calculating the altitude angle of the launching satellite according to the path of the reflected signal to the antenna of the GNSS signal receiver;
and obtaining the phase difference between the reflected signal and the direct signal according to the altitude angle of the transmitting satellite.
Preferably, the first and second electrodes are formed of a metal,
and when the path of the reflected signal reaching the antenna of the GNSS signal receiver is obtained according to the height H from the antenna of the GNSS signal receiver to the reflecting surface and the included angle theta between the direct signal and the reflecting surface, calculating the path of the reflected signal reaching the antenna of the GNSS signal receiver by taking the inclination angle between the ground reflecting surface and the horizontal plane as 0.
Preferably, the method further comprises the following steps:
and screening the original signal-to-noise ratio data to obtain the original signal-to-noise ratio data within a certain altitude angle range.
Humidity inversion device based on GNSS-IR dual-frequency data fusion, the device includes:
the signal-to-noise ratio data acquisition module: the device comprises a GNSS signal receiver, a signal processing module and a signal processing module, wherein the GNSS signal receiver is used for receiving direct signals and corresponding reflected signals of satellites at the same time and obtaining original signal-to-noise ratio data according to the direct signals and the reflected signals;
a characteristic parameter acquisition module: the amplitude and the phase of the first wave band and the amplitude and the phase of the second wave band are respectively obtained through the original signal-to-noise ratio data;
the characteristic parameter fusion module: the amplitude fusion device is used for fusing the amplitudes of the first wave band and the second wave band to obtain fused amplitudes and fusing the phases of the first wave band and the second wave band to obtain fused phases;
a multivariate linear model building module: the multi-element linear model is established according to the fused amplitude and phase;
an inversion module: and substituting the fused amplitude and phase into the multivariate linear model to obtain a corresponding soil humidity value.
A storage medium storing a computer program which, when executed by a processor, implements the steps of the GNSS-IR dual-frequency data fusion based humidity inversion method as described above.
This application adopts above technical scheme, possesses following beneficial effect at least:
this application adopts an antenna to receive satellite's direct incident signal and reflected signal simultaneously and acquires signal to noise ratio data, obtain two characteristic parameter of amplitude and phase place of first wave band and second wave band through signal to noise ratio data, through fusing two characteristic parameter, obtain the characteristic parameter after fusing, establish many first linear model through the characteristic parameter after fusing, carry out the inversion to soil moisture based on many first linear model, compare in the single wave band data measurement among the prior art, this application is through the mode of two wave band data fusion, corresponding data quality is improved to the accessible quality complementation, improve measuring result's precision.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow diagram illustrating a method for humidity inversion based on GNSS-IR dual-frequency data fusion in accordance with an exemplary embodiment;
FIG. 2 is a schematic diagram of a GNSS receiver application scenario according to another exemplary embodiment;
FIG. 3 is a graph illustrating inversion soil moisture contrast before and after fusion for satellite number G13, according to another exemplary embodiment;
FIG. 4 is a system diagram of a GNSS-IR dual-frequency data fusion based humidity inversion apparatus in accordance with another exemplary embodiment;
in the drawings: the system comprises a 1-signal-to-noise ratio data acquisition module, a 2-characteristic parameter acquisition module, a 3-characteristic parameter fusion module, a 4-multivariate linear model building module and a 5-inversion module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without making any creative effort, shall fall within the protection scope of the present application.
Example one
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a humidity inversion method based on GNSS-IR dual-frequency data fusion according to an exemplary embodiment, where the humidity inversion method is applied to the technical field of soil humidity measurement, and the humidity inversion method includes:
s1, simultaneously receiving a direct signal and a corresponding reflected signal of a satellite through a GNSS signal receiver, and obtaining original signal-to-noise ratio data according to the direct signal and the reflected signal;
s2, respectively obtaining the amplitude and the phase of the first wave band and the amplitude and the phase of the second wave band through the original signal-to-noise ratio data;
s3, fusing the amplitudes of the first wave band and the second wave band to obtain fused amplitudes, and fusing the phases of the first wave band and the second wave band to obtain fused phases;
s4, establishing a multi-element linear model according to the fused amplitude and phase;
s5, substituting the fused amplitude and phase into a multi-element linear model to obtain a corresponding soil humidity value;
it can be understood that, this application adopts an antenna to receive satellite's direct projection signal and reflected signal simultaneously and obtains signal to noise ratio data, obtain two characteristic parameters of amplitude and phase place of first wave band and second wave band through signal to noise ratio data, through fusing two characteristic parameters, obtain the characteristic parameter after fusing, establish many first linear models through the characteristic parameter after fusing, carry out the inversion to soil moisture based on many first linear models, compare in the single wave band data measurement among the prior art, this application passes through the mode of two wave band data fusion, corresponding data quality is improved to accessible quality complementation, improve the precision of measuring result.
Preferably, the first and second liquid crystal display panels are,
the obtaining the amplitude and the phase of the first waveband and the amplitude and the phase of the second waveband respectively through the original signal-to-noise ratio data comprises:
processing the original signal-to-noise ratio data to obtain the phase difference between the reflected signal and the direct signal;
removing the influence of the direct signal on the reflected signal in the original signal-to-noise ratio data to obtain a multipath reflected signal;
fitting the multipath reflected signals by a least square method, and simultaneously obtaining the amplitudes and phases corresponding to the first wave band and the second wave band according to the phase difference of the direct signals and the reflected signals;
it will be appreciated that the raw signal-to-noise ratio data can be represented by the following equation:
in the formula, ad and Am represent the amplitudes of the direct and reflected satellite signals, epsilon is the phase difference between the direct and reflected satellite signals, the frequency of the SNR signal-to-noise ratio mainly depends on the relative position relationship among the satellite, the ground reflecting surface and the receiving antenna, the ground reflecting surface and the horizontal plane have a certain inclination angle beta, but the inclination angle generally serves as zero processing because the influence on the whole signal receiving is not large, and delta satisfies the following condition, assuming that the satellite height angle is E, theta is the included angle between the direct and reflecting surfaces, H is the height between the antenna and the reflecting surface, and the propagation path required by the reflected signal to reach the antenna is delta:
(2)：δ＝2H sinθ＝2H sin|E-β|
it is generally considered that the angle between the reflecting surface and the horizontal plane is 0 (θ is approximately equal to 0), and the above equation can be simplified as follows:
(3)：δ＝2H sin E
the phase difference between the reflected signal and the direct signal can be deduced by the three formulas as follows:
so far, the extraction of the phase difference can be completed;
from the above equation, the frequency of the multipath oscillation can be calculated as follows:
the height change of the equivalent antenna is mainly influenced by observation time, and the change of the equivalent antenna is very small in a normal observation time range, so that the change rate of the height of the equivalent antenna can be considered to be negligible, and the formula can be further simplified as follows:
the equation (6) shows that h and f are in linear relation, and the higher the antenna mounting is, the higher the multi-path oscillation frequency is. Because only the multipath reflected signal has a certain relation with the physical quantity of the ground object in the original signal-to-noise ratio data, it is necessary to perform detrending processing on the original signal-to-noise ratio data after performing spectrum analysis on Lomb-Scargle, that is, to remove the influence of the direct signal on reflected signal analysis, and the multipath reflected signal SNRm can be expressed as:
(7)：SNR m ＝A m cos(4πHλ -1 sin E+ε)
and fitting the SNRm data without the direct signal by a least square method to obtain the amplitude Am and the initial phase epsilon corresponding to the first waveband and the second waveband respectively.
Preferably, the first and second electrodes are formed of a metal,
respectively fusing the amplitudes and the phases of the first wave band and the second wave band by adopting a Helmert fusion method;
it can be understood that when the characteristic parameters are fused, the Helmert fusion is selected, and compared with the traditional fusion methods such as average weighted fusion and entropy fusion, the Helmert fusion has the advantages of stable data quality after fusion, high precision improvement and the like, and the fusion method is convenient to operate and high in feasibility.
Preferably, the first and second electrodes are formed of a metal,
performing He ] mert fusion on the amplitudes and phases of the first and second wave bands respectively to obtain fused amplitudes and phases, wherein the fused amplitudes and phases comprise:
respectively establishing random models between the random models and the in-situ soil humidity according to the respective amplitudes and phases of the first wave band and the second wave band;
determining a corresponding coefficient matrix through a random model;
establishing an error equation according to the coefficient matrix;
obtaining a normal equation according to an error equation;
establishing a relation between the residual sum of squares and the unit total variance according to a normal equation;
iterative calculation is carried out on the process from the error equation to the relation between the residual sum of squares and the unit total variance to obtain corresponding weight;
obtaining corresponding fused amplitude and phase through corresponding weights;
it can be understood that the basic idea of Helmert variance component estimation is: the method comprises the steps of firstly determining initial weights of various observed quantities, performing pre-adjustment, estimating the differences before the test of the various observed quantities according to a certain principle by utilizing the sum of squares of residual errors of the various observed quantities obtained after the pre-adjustment, and re-determining the weights. By continuously calculating and iterating in this way, errors in unit weights of different types of observed values tend to be consistent, and therefore the optimal adjustment effect is achieved. According to the basic requirements of Helmert variance component estimation theory, firstly, establishing the difference between the characteristic parameters of the first wave band and the second wave band and the in-situ soil humidityDetermining corresponding coefficient matrixes B1 and B2 by the random model, wherein the observation quantity I comprises two types of independent observation values
And
their weight matrices are respectively
And
and P is 12 =0. I.e. I = [ ] 1 I 2 ] T ，
Here:
let the weight λ of the initialization I1, I2 1 ，λ 2 To 1, the corresponding error equation is:
from the error equations (10), (11), the equation can be derived:
the variance component estimation is to use the sum of squares of various types of correction numbers after each adjustment
And
iterative estimation of the unit weight variance of both observations
And
therefore, it is necessary to establish the relation between the sum of squared residuals and the unit total variance:
the unit weight variance of the two is estimated by:
(18)：S×δ＝Q
and (4) stopping iteration until the unit total variances of the two types of observed values are similar through the iterative calculation of the formulas (8) to (18), obtaining corresponding weights at the moment, and obtaining corresponding fusion values after calculation by using the weights.
Preferably, the first and second liquid crystal display panels are,
the establishing of the multivariate linear model according to the fused amplitude and phase comprises the following steps:
obtaining an observed value of soil humidity according to the fused amplitude and phase;
estimating the observed value to obtain a predicted value of the soil humidity;
obtaining the deviation square sum of the observed value and the estimated value according to the observed value and the estimated value;
according to the principle of a least square method, the sum of squares of the deviations is minimized to obtain a regression coefficient;
establishing a multivariate linear model by taking the fused amplitude and phase as independent variables of the multivariate linear model, taking soil humidity as a dependent variable and the determined regression coefficient;
it can be understood that, using the fused amplitude and phase characteristic parameters to build a multiple linear regression model, the following specific formulas and steps for building the multiple linear model,
the mathematical expression for multiple linear regression can be represented by the following equation:
(19)：Y＝β 0 +β 1 x 1 +β 2 x 2 +Δ
wherein Y represents a dependent variable, x i Denotes the independent variable, beta i Expressing a regression coefficient, and enabling delta to represent a random error term;
defining the interference characteristic parameter of SNRm as independent variable x in soil humidity inversion model i Taking the output soil humidity value as a dependent variable Y, and establishing a linear regression model as a whole is the process of determining a proper regression coefficient;
equation (19) in the above equation can be expressed in a matrix form:
(20)：Y＝Xβ+Δ
wherein Y = [ Y = 1 … y n ] T ，
β＝[β 0 β 1 β 2 ] T ，Δ＝[Δ 0 Δ 1 Δ 2 ] T ；
Estimating unknown parameters by a common least square method;
is provided with
Are respectively the parameter beta 0 ，β 1 ，β 2 Then y obtains an observed value that can be expressed as:
order to
Is y k The estimated values of (c) are:
the result of the above formula is the observed value y k According to the principle of least square method, the smaller the sum of squared deviations should be controlled to be, the better, Z represents the sum:
solving the minimum value of the formula (23), calculating the result, and finally solving to obtain the matrix equation regression coefficient as follows:
in linear regression, by using the judgment coefficient R 2 To express the degree of fitting, the coefficient of judgment R 2 The smaller the fitting degree is, the lower the fitting degree is, the corresponding multiple linear regression model can be obtained by substituting the calculation result of the regression coefficient into the formula (21), and the fused multiple linear regression model is obtainedSubstituting the characteristic parameters as independent variables into the model to obtain corresponding soil humidity values.
Preferably, the first and second liquid crystal display panels are,
the processing the original signal-to-noise ratio data to obtain the phase difference between the reflected signal and the direct signal comprises:
obtaining the path of the reflected signal reaching the antenna of the GNSS signal receiver according to the height H from the antenna of the GNSS signal receiver to the reflecting surface and the included angle theta between the direct signal and the reflecting surface;
calculating the altitude angle of the launching satellite according to the distance of the reflected signal to the antenna of the GNSS signal receiver;
obtaining the phase difference between the reflected signal and the direct signal according to the altitude angle of the transmitting satellite;
it will be appreciated that the frequency of the SNR depends primarily on the relative positions of the satellite, the ground reflecting surface, and the receiving antenna mounting, as shown in fig. 2. The ground reflecting surface and the horizontal plane have a certain inclination angle beta, but the inclination angle does not greatly influence the whole signal reception, so the inclination angle is generally treated as zero, and if the satellite height angle is E, theta is an included angle between a direct signal and the reflecting surface, H is a propagation path required by a highly reflected signal between an antenna and the reflecting surface to reach the antenna, and delta is set as delta, delta satisfies:
(2)：δ＝2H sinθ＝2H sin|E-β|
it is generally considered that the angle between the reflecting surface and the horizontal plane is 0 (θ is approximately equal to 0), and the above equation can be simplified as follows:
(3)：δ＝2H sin E
the phase difference between the reflected signal and the direct signal can be deduced by the three formulas:
the extraction of the phase difference can be completed.
Preferably, the first and second liquid crystal display panels are,
when the path of the reflected signal reaching the antenna of the GNSS signal receiver is obtained according to the height H from the antenna of the GNSS signal receiver to the reflecting surface and the included angle theta between the direct signal and the reflecting surface, the inclination angle between the ground reflecting surface and the horizontal plane is taken as 0 to calculate the path of the reflected signal reaching the antenna of the GNSS signal receiver;
it can be understood that the ground reflecting surface has a certain inclination angle with the horizontal plane, but the inclination angle is not greatly influenced by the overall signal reception, and is generally treated as zero for the convenience of calculation.
Preferably, the method further comprises the following steps:
screening the original signal-to-noise ratio data to obtain the original signal-to-noise ratio data within a certain altitude angle range;
it can be understood that the original signal-to-noise ratio data can be screened according to the altitude angle range, for example, signal-to-noise ratio data of which the altitude angle is within the range of 5 degrees to 20 degrees in the signal-to-noise ratio data is extracted to obtain signal-to-noise ratio data of the G13 satellite;
in order to prove the accuracy of the method on the measurement result, the embodiment selects the data of 19 th to 128 th year of the satellite No. G13 of the P038 site in 2017 in the U.S. PBO site monitoring network to perform the processing and analysis of the steps, and the accuracy improvement effect of the method is as follows:
GNSS signal receiving and processing: acquiring an observation O file and a corresponding ephemeris file N file, merging the files, extracting corresponding GPS signal-to-noise ratio (SNR) data, screening and extracting the SNR data of which the height angle is within the range of 5-20 degrees in the SNR data to obtain the SNR data of a G13 satellite:
characteristic parameter extraction: according to the detailed description of the step, obtaining the characteristic parameters of the amplitude (A113, A213) and the phase (P113, P213) corresponding to the L1 wave band and the L2 wave band of the G13 satellite respectively by performing least square fitting on the signal-to-noise ratio data without the direct signal according to the formulas (1) to (7);
and (3) fusing the characteristic parameters corresponding to the L1 wave band and the L2 wave band: in the step, amplitude (a 113, a 213) and phase (P113, P213) characteristic parameters are fused to obtain corresponding fused amplitude (a 13) and phase (P13) characteristic parameters, so as to facilitate subsequent modeling;
soil humidity inversion: the amplitude and the phase fusion value obtained by fusion are used for establishing a corresponding multivariate linear model, the multivariate linear models corresponding to the L1 and L2 wave bands of the G13 satellite respectively are established, the soil humidity inverted value SM12 after fusion is obtained by model inversion of the soil humidity respectively, and the soil humidity inverted values SM1 and SM2 corresponding to the L1 and L2 wave bands before fusion are established, the corresponding inverted soil humidity comparison graph is shown in figure 3, the soil humidity inverted value SM12 after fusion can be found easily in the figure and is closer to an in-situ soil humidity curve, and the conclusion can also be obtained from the data in the following table:
example two
As shown in fig. 4, this embodiment further provides a system schematic diagram of a humidity inversion apparatus based on GNSS-IR dual-frequency data fusion, where the apparatus includes:
signal-to-noise ratio data acquisition module 1: the device comprises a GNSS signal receiver, a signal processing module and a signal processing module, wherein the GNSS signal receiver is used for receiving direct signals and corresponding reflected signals of satellites at the same time and obtaining original signal-to-noise ratio data according to the direct signals and the reflected signals;
the characteristic parameter obtaining module 2: the amplitude and the phase of the first wave band and the amplitude and the phase of the second wave band are respectively obtained through the original signal-to-noise ratio data;
the characteristic parameter fusion module 3: the amplitude fusion module is used for fusing the amplitudes of the first wave band and the second wave band to obtain fused amplitudes and fusing the phases of the first wave band and the second wave band to obtain fused phases;
the multivariate linear model building module 4: the multi-element linear model is established according to the fused amplitude and phase;
an inversion module 5: the amplitude and the phase after fusion are substituted into the multi-element linear model to obtain a corresponding soil humidity value;
it can be understood that, in the present application, the signal-to-noise ratio data acquisition module 1 is configured to simultaneously receive the direct signal and the corresponding reflected signal of the satellite through the GNSS signal receiver, and obtain the original signal-to-noise ratio data according to the direct signal and the reflected signal; the characteristic parameter acquisition module 2 is used for respectively obtaining the amplitudes and phases of the first wave band and the second wave band through the original signal-to-noise ratio data; the characteristic parameter fusion module 3 is used for respectively fusing the amplitudes and phases of the first wave band and the second wave band to obtain fused amplitudes and phases; the multivariate linear model establishing module 4 is used for establishing a multivariate linear model according to the fused amplitude and phase; the inversion module 5 is used for substituting the fused amplitude and phase into the multivariate linear model to obtain a corresponding soil humidity value; compare in single-band data measurement among the prior art, this application is through the mode of dual-band data fusion, and accessible quality is complementary improves corresponding data quality, improves the precision of measuring result.
EXAMPLE III
The present embodiment further provides a storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps in the method for inverting humidity based on GNSS-IR dual-frequency data fusion as described above;
it will be appreciated that the storage medium referred to above may be a read-only memory, a magnetic or optical disk, or the like.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present application, the meaning of "plurality" means at least two unless otherwise specified.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or intervening elements may also be present; when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present, and further, as used herein, connected may include wirelessly connected; the term "and/or" is used to include any and all combinations of one or more of the associated listed items.
Any process or method descriptions in flow charts or otherwise described herein may be understood as: represents modules, segments or portions of code which include one or more executable instructions for implementing specific logical functions or steps of a process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried out in the method of implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are exemplary and should not be construed as limiting the present application and that changes, modifications, substitutions and alterations in the above embodiments may be made by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. The humidity inversion method based on GNSS-IR dual-frequency data fusion is characterized by comprising the following steps:
simultaneously receiving a direct signal and a corresponding reflected signal of a satellite through a GNSS signal receiver, and obtaining original signal-to-noise ratio data according to the direct signal and the reflected signal;
respectively obtaining the amplitude and the phase of a first wave band and the amplitude and the phase of a second wave band through original signal-to-noise ratio data;
fusing the amplitudes of the first wave band and the second wave band to obtain fused amplitudes, and fusing the phases of the first wave band and the second wave band to obtain fused phases;
establishing a multivariate linear model according to the fused amplitude and phase;
and substituting the fused amplitude and phase into a multivariate linear model to obtain a corresponding soil humidity value.
2. The method of claim 1,
the obtaining the amplitude and the phase of the first waveband and the amplitude and the phase of the second waveband respectively through the original signal-to-noise ratio data comprises:
processing the original signal-to-noise ratio data to obtain the phase difference between the reflected signal and the direct signal;
removing the influence of the direct signal on the reflected signal in the original signal-to-noise ratio data to obtain a multipath reflected signal;
and fitting the multipath reflected signals by a least square method, and obtaining the corresponding amplitudes and phases of the first wave band and the second wave band according to the phase difference of the direct signals and the reflected signals.
3. The method of claim 2,
and respectively fusing the amplitudes and the phases of the first wave band and the second wave band by adopting a Helmert fusion method.
4. The method of claim 3,
performing Helmert fusion on the amplitudes and the phases of the first wave band and the second wave band respectively, and obtaining the fused amplitudes and phases comprises:
respectively establishing random models between the random models and the in-situ soil humidity according to the respective amplitudes and phases of the first wave band and the second wave band;
determining a corresponding coefficient matrix through a random model;
establishing an error equation according to the coefficient matrix;
obtaining a normal equation according to an error equation;
establishing a relation between the residual sum of squares and the unit total variance according to a normal equation;
iterative calculation is carried out on the process from the error equation to the relation between the residual sum of squares and the unit total variance to obtain corresponding weight;
and obtaining the corresponding fused amplitude and phase through the corresponding weight.
5. The method of claim 4,
the establishing of the multivariate linear model according to the fused amplitude and phase comprises the following steps:
obtaining an observed value of soil humidity according to the fused amplitude and phase;
estimating the observed value to obtain a predicted value of the soil humidity;
obtaining the deviation square sum of the observed value and the estimated value according to the observed value and the estimated value;
according to the principle of a least square method, the sum of squares of the deviations is minimized to obtain a regression coefficient;
and establishing the multivariate linear model by taking the fused amplitude and phase as independent variables of the multivariate linear model, taking soil humidity as a dependent variable and the determined regression coefficient.
6. The method of claim 2,
the processing of the original signal-to-noise ratio data to obtain the phase difference between the reflected signal and the direct signal comprises:
obtaining the path of a reflected signal reaching the antenna of the GNSS signal receiver according to the height H from the antenna of the GNSS signal receiver to the reflecting surface and the included angle theta between the direct signal and the reflecting surface;
calculating the altitude angle of the launching satellite according to the distance of the reflected signal to the antenna of the GNSS signal receiver;
and obtaining the phase difference between the reflected signal and the direct signal according to the altitude angle of the transmitting satellite.
7. The method of claim 6,
and when the path of the reflected signal reaching the antenna of the GNSS signal receiver is obtained according to the height H from the antenna of the GNSS signal receiver to the reflecting surface and the included angle theta between the direct signal and the reflecting surface, calculating the path of the reflected signal reaching the antenna of the GNSS signal receiver by taking the inclination angle between the ground reflecting surface and the horizontal plane as 0.
8. The method of claim 1, further comprising:
and screening the original signal-to-noise ratio data to obtain the original signal-to-noise ratio data within a certain altitude angle range.
9. Humidity inversion device based on GNSS-IR dual-frequency data fusion, characterized in that, the device includes:
a signal-to-noise ratio data acquisition module: the device comprises a GNSS signal receiver, a signal processing module and a signal processing module, wherein the GNSS signal receiver is used for receiving direct signals and corresponding reflected signals of satellites at the same time and obtaining original signal-to-noise ratio data according to the direct signals and the reflected signals;
a characteristic parameter acquisition module: the amplitude and the phase of the first wave band and the amplitude and the phase of the second wave band are respectively obtained through the original signal-to-noise ratio data;
a characteristic parameter fusion module: the amplitude fusion device is used for fusing the amplitudes of the first wave band and the second wave band to obtain fused amplitudes and fusing the phases of the first wave band and the second wave band to obtain fused phases;
a multivariate linear model building module: the multi-element linear model is established according to the fused amplitude and phase;
an inversion module: and substituting the fused amplitude and phase into the multivariate linear model to obtain a corresponding soil humidity value.
10. A storage medium storing a computer program which, when executed by a processor, performs the steps of the method for GNSS-IR dual-frequency data fusion based humidity inversion according to any of claims 1 to 8.
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| CN116643029A (en) * | 2023-07-27 | 2023-08-25 | 中国科学院地理科学与资源研究所 | Method and system for monitoring soil salinity by using foundation GNSS-IR data |
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| CN116643029A (en) * | 2023-07-27 | 2023-08-25 | 中国科学院地理科学与资源研究所 | Method and system for monitoring soil salinity by using foundation GNSS-IR data |
| CN116643029B (en) * | 2023-07-27 | 2023-09-26 | 中国科学院地理科学与资源研究所 | Method and system for monitoring soil salinity by using foundation GNSS-IR data |
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